Advanced Systems and Methods for Virtual Agronomic Sensing

ABSTRACT

Systems and methods for virtual agronomic sensing are provided. In embodiments methods comprise receiving first agronomic data for a first geographic location comprising sensor data from agronomic sensors at the first geographic location; receiving first agronomic information for the first geographic location; and generating first predictive agronomic data for the first geographic location using the first agronomic data for the first geographic location and the first agronomic information for the first geographic location. Methods may further comprise testing the first predictive agronomic data for the first geographic location to be used for a second geographic location; receiving second agronomic information for the second geographic location; generating virtual agronomic sensors for the second geographic location as a function of the testing the first predictive agronomic data for the first geographic location; and providing second predictive agronomic data for the second geographic location using the virtual agronomic sensors.

CROSS REFERENCE TO RELATED APPLICATION

This continuation application claims the priority benefit of U.S.Non-Provisional application Ser. No. 16/553,007 filed on Aug. 27, 2019and titled “Systems and Methods for Virtual Agronomic Sensing,” whichclaims the priority benefit of U.S. Provisional Patent Application Ser.No. 62/726,273 filed on Sep. 2, 2018, and titled “Systems and Methodsfor Virtual Agronomic Sensing,” all of which are hereby incorporated byreference in their entireties.

FIELD OF THE TECHNOLOGY

Embodiments of the disclosure relate to virtual agronomic sensing. Inparticular, the present disclosure relates to systems and methods forvirtual agronomic sensing using agronomic sensors.

SUMMARY

In some embodiments the present disclosure is directed to a system ofone or more computers which can be configured to perform particularoperations or actions by virtue of having software, firmware, hardware,or a combination thereof installed on the system that in operationcauses or cause the system to perform actions and/or method steps asdescribed herein.

Various embodiments of the present technology include a system forvirtual agronomic sensing, comprising: (a) one or more agronomic sensorsat a first geographic location; (b) at least one processor; and (c) amemory storing processor-executable instructions, wherein the at leastone processor is configured to implement the following operations uponexecuting the processor-executable instructions: (i) receiving firstagronomic data for the first geographic location, the first agronomicdata for the first geographic location comprising sensor data from theone or more agronomic sensors at the first geographic location; (ii)receiving first agronomic information for the first geographic location;(iii) generating first predictive agronomic data for the firstgeographic location using the first agronomic data for the firstgeographic location and the first agronomic information for the firstgeographic location; (iv) testing the first predictive agronomic datafor the first geographic location to be used for a second geographiclocation; (v) receiving second agronomic information for the secondgeographic location; (vi) generating one or more virtual agronomicsensors for the second geographic location as a function of the testingthe first predictive agronomic data for the first geographic location;and (vii) providing second predictive agronomic data for the secondgeographic location using the one or more virtual agronomic sensors forthe second geographic location.

In various embodiments the one or more agronomic sensors at the firstgeographic location comprise water pressure sensors in an irrigationsystem, the water pressure sensors directly measuring water delivery inthe irrigation system and monitoring water leaks in the irrigationsystem.

In some embodiments the water pressure sensors in the irrigation systemare correlated with satellite imaging.

In various embodiments the one or more agronomic sensors at the firstgeographic location comprise soil moisture sensors; wherein the firstagronomic data for the first geographic location comprises soil moisturecontent; wherein the first agronomic information for the firstgeographic location comprises soil type and rainfall; wherein the firstpredictive agronomic data for the first geographic location comprisessoil moisture content; wherein the second agronomic information for thesecond geographic location comprises soil type and rainfall; wherein theone or more virtual agronomic sensors for the second geographic locationcomprise virtual soil moisture sensors; and wherein the secondpredictive agronomic data for the second geographic location comprisesirrigation levels.

In some embodiments wherein the one or more agronomic sensors at thefirst geographic location comprise frost sensors; wherein the firstagronomic data for the first geographic location comprises frostformation; wherein the first agronomic information for the firstgeographic location comprises weather data; wherein the first predictiveagronomic data for the first geographic location comprises frostformation; wherein the second agronomic information for the secondgeographic location comprises weather data; wherein the one or morevirtual agronomic sensors for the second geographic location comprisefrost sensors; and wherein the second predictive agronomic data for thesecond geographic location comprises frost formation.

In some embodiments the weather data comprises air temperature,humidity, and frost formation.

In various embodiments wherein the one or more agronomic sensors at thefirst geographic location comprise leaf temperature sensors; wherein thefirst agronomic data for the first geographic location comprises leaftemperature, the leaf temperature indicating crop stress; wherein thefirst agronomic information for the first geographic location comprisesweather data; wherein the first predictive agronomic data for the firstgeographic location comprises leaf temperature, the leaf temperatureindicating crop stress; wherein the second agronomic information for thesecond geographic location comprises weather data; wherein the one ormore virtual agronomic sensors for the second geographic locationcomprise virtual leaf temperature sensors; and wherein the secondpredictive agronomic data for the second geographic location comprisescrop stress.

In some embodiments wherein the one or more agronomic sensors at thefirst geographic location comprise weather sensors; wherein the firstagronomic data for the first geographic location comprises weather dataand satellite images; wherein the first agronomic information for thefirst geographic location comprises historical crop yield for the firstgeographic location; wherein the first predictive agronomic data for thefirst geographic location comprises crop yield for the first geographiclocation; wherein the second agronomic information for the secondgeographic location comprises weather data and satellite images; whereinthe one or more virtual agronomic sensors for the second geographiclocation comprise virtual weather sensors; and wherein the secondpredictive agronomic data for the second geographic location comprisescrop yield prediction.

In various embodiments the at least one processor is further configuredto implement the operation of receiving additional relevant data for thesecond geographic location; wherein the generating the one or morevirtual agronomic sensors for the second geographic location is furthera function of the additional relevant data for the second geographiclocation; and wherein the providing the second predictive agronomic datafor the second geographic location uses the additional relevant data forthe second geographic location.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed disclosure, and explainvarious principles and advantages of those embodiments.

The methods and systems disclosed herein have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present disclosure so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

FIG. 1 is a simplified diagram of water percolation in multiple soillayers according to exemplary embodiments of the present technology.

FIG. 2 is a simplified diagram of water percolation in a single soillayer according to exemplary embodiments of the present technology.

FIG. 3 is a diagram of water percolation formulas according to exemplaryembodiments of the present technology.

FIG. 4 is a simplified diagram of a yield prediction model according toexemplary embodiments of the present technology.

FIG. 5 is a simplified diagram of yield increases by balancing wateraccording to exemplary embodiments of the present technology.

FIG. 6 illustrates a computer system used to practice aspects of thepresent technology.

FIG. 7 illustrates an exemplary method according to exemplaryembodiments of the present technology.

DETAILED DESCRIPTION

While this technology is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail several specific embodiments with the understanding that thepresent disclosure is to be considered as an exemplification of theprinciples of the technology and is not intended to limit the technologyto the embodiments illustrated.

FIG. 1 is a simplified diagram of water percolation in multiple soillayers according to exemplary embodiments of the present technology. Forexample, in some embodiments, the one or more agronomic sensors at thefirst location comprise: soil moisture sensors. Simulation of waterpercolation in multiple soil layers is accomplished by gathering varioussoil and weather measurements and is used for providing predicativeagronomic data. For example, the various measurements include an amountof input water from rainfall and irrigation [R(t)], evapotranspiration[ET₁(t, θ(t))], amount of runoff water on the ground surface [Q_(s)(t)],soil moisture ratio at a layer (i) [θ_(i)(t)], infiltration intensity[g(t)], and amount of percolation towards underground at the layer (i)[Fi(t)].

FIG. 2 is a simplified diagram of water percolation in a single soillayer according to exemplary embodiments of the present technology.Simulation of water percolation in a single soil layer is accomplishedby gathering various soil measurements and is used for providingpredicative agronomic data. For example, an amount of input water fromrainfall and irrigation [R(t)], evapotranspiration [ET₁(t, θ(t))],amount of runoff water on the ground surface [Q_(s)(t)], infiltrationintensity [g(t)], and amount of percolation towards underground at thelayer [Fi(t)] are used to simulate water percolation in a single soillayer (e.g., layer (i)).

FIG. 3 is a diagram of water percolation formulas according to exemplaryembodiments of the present technology. In various embodiments a waterbalance formula and a soil moisture prediction formula are used forproviding predicative agronomic data. For example, a water balanceformula is as follows:

Q _(s)(t)=R(t)−g(t)−ET(t)−F _(i)(t)

In some embodiments, the water balance formula evaluates water balanceof a soil environment system by calculating surplus water from excessiveirrigation. For example, soil moisture prediction formula is as follows:

θ(t+Δt)=θ(t)+∫_(t) ^(t+Δt) g(t)dt−∫ _(t) ^(t+Δt) ET(t)dt−∫ _(t) ^(t+Δt)F(t)dt

In various embodiments, the soil moisture prediction formula evaluateseach factor of soil moisture change by calculating an amount of soilmoisture change. Furthermore, the soil moisture prediction formulaestimates how much water is flowing from irrigation or rainfall andpercolating into a deeper layer of soil.

FIG. 4 is a simplified diagram of a yield prediction model according toexemplary embodiments of the present technology. In some embodimentsinput data is feed into a prediction model to produce a yieldprediction. A yield prediction model is trained with satellite images,weather reports and orchard locations as input data, and historicalyield data as output data by using a Hierarchical Bayesian model. Insome embodiments, the yield prediction is extended from a field level toan entire area of the field as well as other fields.

FIG. 5 is a simplified diagram of yield increases by balancing wateraccording to exemplary embodiments of the present technology. In variousembodiments, the one or more agronomic sensors at the first locationcomprise water pressure sensors in an irrigation system, the waterpressure sensors directly measuring water delivery in the irrigationsystem and monitoring water leaks in the irrigation system. For example,the water pressure sensors monitor flow from a pump in the irrigationsystem. In exemplary embodiments, an increase in pressure sensor densityis a direct measure of water delivery and quickly detects leaks andobstructions in the irrigation system as well as imbalanced waterdelivery. In some embodiments, the water pressure sensors measure anamount of water in the irrigation system that is correlated withsatellite imaging. In various embodiments yield is increased bybalancing water delivery in the irrigation system. For example,increased yields are accomplished by maximizing effectiveness of waterand nutrients applied by a combination of granular sensing of soil,virtual soil, water pressure, plant sensors, and satellite images. Invarious embodiments, varying granularity of various combinations ofsensing of soil, virtual soil, water pressure, plant sensors, andsatellite images are used to maximize effectiveness of water andnutrients to produce increased yields.

FIG. 6 is a diagrammatic representation of an example machine in theform of a computer system 1, within which a set of instructions forcausing the machine to perform any one or more of the methodologiesdiscussed herein may be executed. In various example embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be amicroprocessor chip or system on a chip (SOC) personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), acellular telephone, a smart phone with combination of said functions aportable music player (e.g., a portable hard drive audio device such asan Moving Picture Experts Group Audio Layer 3 (MP3) player), a webappliance, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The example computer system 1 includes a processor or multipleprocessor(s) 5 (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both), and a main memory 10 and static memory15, which communicate with each other via a bus 20. The computer system1 may further include a video display 35 (e.g., a liquid crystal display(LCD)). The computer system 1 may also include an alpha-numeric inputdevice(s) 30 (e.g., a keyboard), a cursor control device (e.g., amouse), a voice recognition or biometric verification unit (not shown),a drive unit 37 (also referred to as disk drive unit), a signalgeneration device 40 (e.g., a speaker), and a network interface device45. The computer system 1 may further include a data encryption module(not shown) to encrypt data.

The disk drive unit 37 includes a computer or machine-readable medium 50on which is stored one or more sets of instructions and data structures(e.g., instructions 55) embodying or utilizing any one or more of themethodologies or functions described herein. The instructions 55 mayalso reside, completely or at least partially, within the main memory 10and/or within the processor(s) 5 during execution thereof by thecomputer system 1. The main memory 10 and the processor(s) 5 may alsoconstitute machine-readable media.

The instructions 55 may further be transmitted or received over anetwork via the network interface device 45 utilizing any one of anumber of well-known transfer protocols (e.g., Hyper Text TransferProtocol (HTTP)). While the machine-readable medium 50 is shown in anexample embodiment to be a single medium, the term “computer-readablemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable medium” shall also be taken to include any mediumthat is capable of storing, encoding, or carrying a set of instructionsfor execution by the machine and that causes the machine to perform anyone or more of the methodologies of the present application, or that iscapable of storing, encoding, or carrying data structures utilized by orassociated with such a set of instructions. The term “computer-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, optical and magnetic media, and carrier wavesignals. Such media may also include, without limitation, hard disks,floppy disks, flash memory cards, digital video disks, random accessmemory (RAM), read only memory (ROM), and the like. The exampleembodiments described herein may be implemented in an operatingenvironment comprising software installed on a computer, in hardware, orin a combination of software and hardware.

One skilled in the art will recognize that the Internet service may beconfigured to provide Internet access to one or more computing devicesthat are coupled to the Internet service, and that the computing devicesmay include one or more processors, buses, memory devices, displaydevices, input/output devices, and the like. Furthermore, those skilledin the art may appreciate that the Internet service may be coupled toone or more databases, repositories, servers, and the like, which may beutilized in order to implement any of the embodiments of the disclosureas described herein.

FIG. 7 illustrates an exemplary method according to exemplaryembodiments of the present technology. FIG. 7 shows a method 700 forvirtual agronomic sensing, including the following steps. The method 700of FIG. 7 shows receiving 710 first agronomic data for a firstgeographic location, the first agronomic data for the first geographiclocation comprising sensor data from one or more agronomic sensors atthe first geographic location. The method 700 of FIG. 7 shows receiving720 first agronomic information for the first geographic location. Themethod 700 of FIG. 7 further shows generating 730 first predictiveagronomic data for the first geographic location using the firstagronomic data for the first geographic location and the first agronomicinformation for the first geographic location. The method 700 of FIG. 7shows testing 740 the first predictive agronomic data for the firstgeographic location to be used for a second geographic location. Themethod 700 of FIG. 7 further shows receiving 750 second agronomicinformation for the second geographic location. The method 700 of FIG. 7shows generating 760 one or more virtual agronomic sensors for thesecond geographic location as a function of the testing the firstpredictive agronomic data for the first geographic location; andproviding 770 second predictive agronomic data for the second geographiclocation using the one or more virtual agronomic sensors for the secondgeographic location.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present technology has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the present technology in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the presenttechnology. Exemplary embodiments were chosen and described in order tobest explain the principles of the present technology and its practicalapplication, and to enable others of ordinary skill in the art tounderstand the present technology for various embodiments with variousmodifications as are suited to the particular use contemplated.

Aspects of the present technology are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thepresent technology. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present technology. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

In the following description, for purposes of explanation and notlimitation, specific details are set forth, such as particularembodiments, procedures, techniques, etc. in order to provide a thoroughunderstanding of the present invention. However, it will be apparent toone skilled in the art that the present invention may be practiced inother embodiments that depart from these specific details.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” or“according to one embodiment” (or other phrases having similar import)at various places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments. Furthermore, depending on the context ofdiscussion herein, a singular term may include its plural forms and aplural term may include its singular form. Similarly, a hyphenated term(e.g., “on-demand”) may be occasionally interchangeably used with itsnon-hyphenated version (e.g., “on demand”), a capitalized entry (e.g.,“Software”) may be interchangeably used with its non-capitalized version(e.g., “software”), a plural term may be indicated with or without anapostrophe (e.g., PE's or PEs), and an italicized term (e.g., “N+1”) maybe interchangeably used with its non-italicized version (e.g., “N+1”).Such occasional interchangeable uses shall not be consideredinconsistent with each other.

Also, some embodiments may be described in terms of “means for”performing a task or set of tasks. It will be understood that a “meansfor” may be expressed herein in terms of a structure, such as aprocessor, a memory, an I/O device such as a camera, or combinationsthereof. Alternatively, the “means for” may include an algorithm that isdescriptive of a function or method step, while in yet other embodimentsthe “means for” is expressed in terms of a mathematical formula, prose,or as a flow chart or signal diagram.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

It is noted at the outset that the terms “coupled,” “connected”,“connecting,” “electrically connected,” etc., are used interchangeablyherein to generally refer to the condition of beingelectrically/electronically connected. Similarly, a first entity isconsidered to be in “communication” with a second entity (or entities)when the first entity electrically sends and/or receives (whetherthrough wireline or wireless means) information signals (whethercontaining data information or non-data/control information) to thesecond entity regardless of the type (analog or digital) of thosesignals. It is further noted that various figures (including componentdiagrams) shown and discussed herein are for illustrative purpose only,and are not drawn to scale.

While specific embodiments of, and examples for, the system aredescribed above for illustrative purposes, various equivalentmodifications are possible within the scope of the system, as thoseskilled in the relevant art will recognize. For example, while processesor steps are presented in a given order, alternative embodiments mayperform routines having steps in a different order, and some processesor steps may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or sub-combinations. Each of theseprocesses or steps may be implemented in a variety of different ways.Also, while processes or steps are at times shown as being performed inseries, these processes or steps may instead be performed in parallel,or may be performed at different times.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. The descriptions are not intended to limit the scope of theinvention to the particular forms set forth herein. To the contrary, thepresent descriptions are intended to cover such alternatives,modifications, and equivalents as may be included within the spirit andscope of the invention as defined by the appended claims and otherwiseappreciated by one of ordinary skill in the art. Thus, the breadth andscope of a preferred embodiment should not be limited by any of theabove-described exemplary embodiments.

What is claimed is:
 1. A system for virtual agronomic sensing,comprising: one or more agronomic sensors at a first geographiclocation; at least one processor; and a memory storingprocessor-executable instructions, wherein the at least one processor isconfigured to implement the following operations upon executing theprocessor-executable instructions: receiving first agronomic data forthe first geographic location, the first agronomic data for the firstgeographic location comprising sensor data from the one or moreagronomic sensors at the first geographic location; receiving firstagronomic information for the first geographic location; and generatingfirst predictive agronomic data for the first geographic location usingthe first agronomic data for the first geographic location and the firstagronomic information for the first geographic location.
 2. The systemfor virtual agronomic sensing of claim 1, wherein the one or moreagronomic sensors at the first geographic location comprise waterpressure sensors in an irrigation system, the water pressure sensorsdirectly measuring water delivery in the irrigation system andmonitoring water leaks in the irrigation system.
 3. The system forvirtual agronomic sensing of claim 2, wherein the water pressure sensorsin the irrigation system are correlated with satellite imaging.
 4. Thesystem for virtual agronomic sensing of claim 1, wherein the one or moreagronomic sensors at the first geographic location comprise soilmoisture sensors and soil percolation sensors; wherein the firstagronomic data for the first geographic location comprises soil moisturecontent; wherein the first agronomic information for the firstgeographic location comprises soil type and rainfall; and wherein thefirst predictive agronomic data for the first geographic locationcomprises soil moisture content.
 5. The system for virtual agronomicsensing of claim 1, wherein the one or more agronomic sensors at thefirst geographic location comprise frost sensors; wherein the firstagronomic data for the first geographic location comprises frostformation; wherein the first agronomic information for the firstgeographic location comprises weather data; and wherein the firstpredictive agronomic data for the first geographic location comprisesfrost formation.
 6. The system for virtual agronomic sensing of claim 5,wherein the weather data comprises air temperature, humidity, and frostformation.
 7. The system for virtual agronomic sensing of claim 1,wherein the one or more agronomic sensors at the first geographiclocation comprise leaf temperature sensors; wherein the first agronomicdata for the first geographic location comprises leaf temperature, theleaf temperature indicating crop stress; wherein the first agronomicinformation for the first geographic location comprises weather data;and wherein the first predictive agronomic data for the first geographiclocation comprises leaf temperature, the leaf temperature indicatingcrop stress.
 8. The system for virtual agronomic sensing of claim 1,wherein the one or more agronomic sensors at the first geographiclocation comprise weather sensors; wherein the first agronomic data forthe first geographic location comprises weather data and satelliteimages; wherein the first agronomic information for the first geographiclocation comprises historical crop yield for the first geographiclocation; and wherein the first predictive agronomic data for the firstgeographic location comprises crop yield for the first geographiclocation.
 9. The system for virtual agronomic sensing of claim 1,wherein the at least one processor is further configured to implement anoperation of receiving additional relevant data for a second geographiclocation.
 10. A method for virtual agronomic sensing, the methodcomprising: receiving first agronomic data for a first geographiclocation, the first agronomic data for the first geographic locationcomprising sensor data from one or more agronomic sensors at the firstgeographic location; receiving first agronomic information for the firstgeographic location; and generating first predictive agronomic data forthe first geographic location using the first agronomic data for thefirst geographic location and the first agronomic information for thefirst geographic location.
 11. The method for virtual agronomic sensingof claim 10, wherein the one or more agronomic sensors at the firstgeographic location comprise water pressure sensors in an irrigationsystem, the water pressure sensors directly measuring water delivery inthe irrigation system and monitoring water leaks in the irrigationsystem.
 12. The method for virtual agronomic sensing of claim 11,wherein the water pressure sensors in the irrigation system arecorrelated with satellite imaging.
 13. The method for virtual agronomicsensing of claim 10, wherein the one or more agronomic sensors at thefirst geographic location comprise soil moisture sensors and soilpercolation sensors; wherein the first agronomic data for the firstgeographic location comprises soil moisture content; wherein the firstagronomic information for the first geographic location comprises soiltype and rainfall; and wherein the first predictive agronomic data forthe first geographic location comprises soil moisture content.
 14. Themethod for virtual agronomic sensing of claim 10, wherein the one ormore agronomic sensors at the first geographic location comprise frostsensors; wherein the first agronomic data for the first geographiclocation comprises frost formation; wherein the first agronomicinformation for the first geographic location comprises weather data;and wherein the first predictive agronomic data for the first geographiclocation comprises frost formation.
 15. The method for virtual agronomicsensing of claim 14, wherein the weather data comprises air temperature,humidity, and frost formation.
 16. The method for virtual agronomicsensing of claim 10, wherein the one or more agronomic sensors at thefirst geographic location comprise leaf temperature sensors; wherein thefirst agronomic data for the first geographic location comprises leaftemperature, the leaf temperature indicating crop stress; wherein thefirst agronomic information for the first geographic location comprisesweather data; and wherein the first predictive agronomic data for thefirst geographic location comprises leaf temperature, the leaftemperature indicating crop stress.
 17. The method for virtual agronomicsensing of claim 10, wherein the one or more agronomic sensors at thefirst geographic location comprise weather sensors; wherein the firstagronomic data for the first geographic location comprises weather dataand satellite images; wherein the first agronomic information for thefirst geographic location comprises historical crop yield for the firstgeographic location; and wherein the first predictive agronomic data forthe first geographic location comprises crop yield for the firstgeographic location.
 18. The method for virtual agronomic sensing ofclaim 10, further comprising receiving additional relevant data for asecond geographic location.
 19. A non-transitory computer readablemedium having embodied thereon instructions being executable by at leastone processor to perform operations for virtual agronomic sensing, theoperations comprising: receiving first agronomic data for a firstgeographic location, the first agronomic data for the first geographiclocation comprising sensor data from one or more agronomic sensors atthe first geographic location; receiving first agronomic information forthe first geographic location; and generating first predictive agronomicdata for the first geographic location using the first agronomic datafor the first geographic location and the first agronomic informationfor the first geographic location.
 20. The non-transitory computerreadable medium of claim 19, wherein the operations further comprisereceiving additional relevant data for a second geographic location.